Engineering Hacks for Deep Learning

Training convolutional neural networks is shockingly easy and becoming more accessible than ever, with great open source tools and even pre-trained models that work out-of-the box. But what do you do when your data becomes too big to fit in memory, or you just don't have enough labeled data to learn what you want? This talk will explore some strategies to speed up training of your AI.

Andrew Doyle

McGill Centre for Integrative Neuroscience

Andrew is a Research Software Developer at the McGill Centre for Integrative Neuroscience the Montreal Neurological Institute. With two Electrical Engineering degrees from McGill, his main interests are in using machine learning to discover models for how the brain functions in data-driven, unsupervised manner.